Third, we found that the models trained with the P-FDB loss were able to maintain long-term consistency to some extent. An example is shown in Supplementary Video 4 and Fig. REF . When the camera moved from left to right beside a pillar in a scene, a part of the scene was occluded by the pillar for some time and then reappeared. After reappearing, the regions before occlusion were stylized in a consistent manner. By contrast, in the absence of the P-FDB loss, the stripes in the red box before occlusion disappeared after this region reappeared. We note that unlike a previous study [1]}, we did not impose any explicit mechanism on the proposed model to ensure long-term consistency. We speculate that this long-term consistency is a result of learned resistance to the disturbance of input: if the input stays the same, the stylized output is also trained to stay the same.
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